In order to address the customerâ€™s satisfaction, mobile operators try to find out what therncustomer needs and what quality makes the customer satisfied. The customer satisfactionrncan be measured or estimated by Quality of Experience (QoE) measurement. Its estimationrnrnand measurement is important to identify the network problems, to understandrncauses and contributing factors. rnWeb browsing is one of the widely used application on Long Term Evolution (LTE) networks.rnrnTherefore, it is essential for service providers to ensure a better QoE on webrnbrowsing service. Web QoE can measure the user satisfaction by subjective or objectivernmeasurement. Subjective test suffers from some drawbacks, such as it has high cost inrnterms of time, money, and manual effort and also cannot be used for real-time QoE evaluation.rnInrnEthiopiarnonlyrnsubjectivernmeasurementrnisrnused,rntornknowrnthernlevelrnofrncustomerrnrnsatisfaction.rnDuerntornthat,rntherncompanyrnisrnexposedrnforrnhighrnexpensesrnandrnalsorncanrnnotrnperformrnthernrealrntimernmeasurementrnofrnQoE.rnrnTo overcome the problem on subjective test, this thesis developed a web browsing QoErnmodel, using Neural Network algorithm that is implemented in matlab software. Thernmodel takes the following QoS metrics as input parameters: page response delay, pagerncontent browsing delay and page download throughput. The model map these metricsrnto QoE interms of Mean Opinion Score (MOS). rnThe model performed an estimation of QoE with a Mean Square Error (MSE) of 0.002 andrncorrelation of 97.2%, relatively to the target QoE. As the result indicates, the estimatedrnand measured QoE values are highly correlated. And the error between them is very low.rnSo, this model can be used for estimating the web browsing QoE for the mobile operators,rnto get objective measurement advantages. Also, it can be used for operators to identifyrnthe network factors that most influence the web browsing QoE.